Manufacturers and distributors of retail products generally monitor product sales in order to maintain proper inventory and to direct marketing efforts. While direct sales from the manufacturer or distributors provide certain useful information, data on ultimate sales to consumers are needed to fully plan inventory and marketing efforts. Monitoring of ultimate sales may be done by sampling sales at retail outlets and transferring sales data to a central point for evaluation. Retail outlets usually cooperate in providing sales data but a significant number of retail outlets are not able to or do not elect to have sales data sampled in a form needed for analysis. As a result, it is necessary to estimate product sales of unsampled individual outlets to provide marketing information.
Information on ultimate sales can be especially critical during certain market events—the launch of a new product into the marketplace, the withdrawal of a key product in the market, or the initiation of a major marketing campaign by one of the major competitors in the marketplace. These situations are certainly of major importance within the pharmaceutical industry. For example, in order to monitor the success of a new product release, or the effectiveness of promotional activity, it is desirable to determine sales volume on a daily basis. However, where the number of sales outlets is large and diverse, it is difficult to quickly gather sales data from all outlets to determine total daily sales volume on a timely basis.
Estimation of prescribing activity is carried out by marketing research practitioners based on ratio estimators and inflation factor estimators as commonly described in such texts as “Sampling Techniques” by W. G. Cochran, John Wiley, New York 1977. These methods attempt to estimate the activity in a pre-established geographic area of known dimensions by scaling up a sample of activity within the area in proportion to the level of a known auxiliary variable (i.e., ratio estimate) or in proportion to the level of sample coverage (via an inflation factor) for the entire area. Typical geographic areas are national or, in some few cases, regional. Such geographic-based methods must assume that the proportion of the total activity that is captured in the sample data (i.e., the captured proportion of the total prescription activity) is accurately represented by either the proportion of the known auxiliary variable captured by the sample data (when the ratio estimate method is applied) or the proportion of total outlets captured by the sample data (when the inflation factor method is applied).
In many cases sales estimates can be accurately projected based on historical data. The more stable the history, the more accurate such projections tend to be. In times of market flux, however, the underlying assumptions of the ratio estimation and inflation factor methods are likely violated and result in biased estimates of prescription activity.
It is known that data from a relatively small sample can be projected to estimate a total quantity by using a weighting factor. The quality of the estimation depends on the accuracy of the reported data, the representative nature of the sample to the total population and the appropriateness of the weighting factor.
A common approach to projecting data from a sample is to weight the sample data
by either:
            N      n        =                            total          ⁢                                          ⁢          number          ⁢                                          ⁢          of          ⁢                                          ⁢          units          ⁢                                          ⁢          in          ⁢                                          ⁢          the          ⁢                                          ⁢          population                          number          ⁢                                          ⁢          of          ⁢                                          ⁢          units          ⁢                                          ⁢          in          ⁢                                          ⁢          the          ⁢                                          ⁢          sample                    ⁢                          ⁢      or                                    ∑          N                ⁢                                  ⁢        Xi                              ∑          n                ⁢                                  ⁢        Xi              =                  total        ⁢                                  ⁢        auxilliary        ⁢                                  ⁢        size        ⁢                                  ⁢        for        ⁢                                  ⁢        units        ⁢                                  ⁢        in        ⁢                                  ⁢        the        ⁢                                  ⁢        population                    auxilliary        ⁢                                  ⁢        size        ⁢                                  ⁢        for        ⁢                                  ⁢        units        ⁢                                  ⁢        in        ⁢                                  ⁢        the        ⁢                                  ⁢        sample            where Xi is some measure on the unit correlated with the variable which is of interest. For example, in the case of pharmaceutical sales data, the units generally refer to individual retail outlets (pharmacies) and Xi is typically the total purchase volume or total prescription volume for a pharmacy. However, a problem with such an approach is that the results can become highly variable when the number of units actually sampled is statistically small, resulting in a large projection factor, or potentially biased when the sample is not representative of the population or when there is little to no correlation between the measure of size, Xi, and the product of interest. This is very likely the case in times of market flux.
In the case of retail facilities, it is not uncommon for retail stores to report daily sales volume of a product in weekly batches which are delayed by several days or more. It is far more difficult, however, to obtain the cooperation of a large number of retailers to provide this data in “real time” at the close of business each day. Thus, when projecting estimates based on the large sample of delayed reporting, the data that are being used are somewhat stale and when basing projections on the real time data, the sampling pool tends to be relatively small. Each of these conditions can lead to inaccuracies in the resulting projection. Thus, there remains a need to provide improved systems and methods for estimating daily sales volume based on sampled data.